Статьи журнала - International Journal of Modern Education and Computer Science
Все статьи: 1064
Robust Algorithm for Face Detection in Color Images
Статья научная
Robust Algorithm is presented for frontal face detection in color images. Face detection is an important task in facial analysis systems in order to have a priori localized faces in a given image. Applications such as face tracking, facial expression recognition, gesture recognition, etc., for example, have a pre-requisite that a face is already located in the given image or the image sequence. Facial features such as eyes, nose and mouth are automatically detected based on properties of the associated image regions. On detecting a mouth, a nose and two eyes, a face verification step based on Eigen face theory is applied to a normalized search space in the image relative to the distance between the eye feature points. The experiments were carried out on test images taken from the internet and various other randomly selected sources. The algorithm has also been tested in practice with a webcam, giving (near) real-time performance and good extraction results.
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Статья научная
In the choice process of optimal military commu-nication (MC) alternative, evaluation data mainly come from expert judgments, simulation results and test bed data, and they cannot be directly used in evaluation because of differences in form and attribute; and the MC environment changes rapidly as the operation tempo increasing. It is an important effort to judge the effectiveness robustness of MC alternative, since both the evaluation data and the MC envi-ronment are full of uncertainty. A robustness evaluation method based on multiple data sources and Monte Carlo simluation is proposed with respect to the characteristics of them. Mainly include Belief map as data expression form; Regression relational model built with Support Vector Re-gression (SVR) to acquire simulation data’s confidence with test bed data as training example; Extensive Bayesian Algo-rithm (EBA) to fuse data from multiple sources; Beta distri-bution fitting method for each criterion of each alternative by using the fused results; and calculation of the Probability of Best (PoB) of each alternative through Monte Carlo simu-lation. Take MCE evaluation of a Naval Vessels Fleet as an example, the proposed method is compared with some gen-eral methods. The results indicate that the proposed method helps to obtain relatively conservative alternative and is effective in guaranteeing the robustness.
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Rough Set and Genetic Based Approach for Maximization of Weighted Association Rules
Статья научная
The present paper proposes a new approach for the effective weighted association rule mining. The proposed approach utilizes the power of Rough Set Theory for obtaining reduct of the targeted dataset. Additionally, approach takes the benefit for weighted measures and the Genetic Algorithm for the generation of the desired set of rules. Enough analysis of proposed approach has been done and observed that the approach works as per the expectation and will be beneficial in situation when there is a requirement for the consideration of hidden rules(maximizing generated rules) in decision-making process.
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Rule Based Communication Protocol between Social Networks using Semantic Web Rule Language (SWRL)
Статья научная
Social network sites have become de factor in fostering human relationships and business prospects. Several social networks abound with little interoperability functionality that enables exchange of profiles of users. Though, proprietary Application Programming Interfaces (APIs) are provided as endpoints for applications in retrieval of user profile. Moreover, semantic web Friend of a Friend (FOAF) is now been used as a medium for realizing semantic social networks to be able to share user's profile across sites. And since the goal of semantic web is to provide autonomous data centric system coupled on ontology and reasoning, we propose a novel communication protocol named iProc, and usable by autonomous agents that relies on the distributive nature of social network data in coalescing a virtually centralized social network and as well providing means to enlarge user's connectivity to other users across different sites. This paper presents the architecture for a proposed iProc. Furthermore, an implementation of the FOAF files to be used was carried out and discussed.
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Rural Empowerment through Education: Case Study of a Learning Community Telecentre in Indonesia
Статья научная
This research aims to investigate the impact of information and communication technologies (ICTs) for development and social change in a rural telecentre in Indonesia. It attempts to contribute to the literature of ICTs for development and social change, in the field of analysis of a community-driven ICTs initiative and the analysis of an Indonesian telecentre, two research areas that had not been extensively explored. A qualitative case study approach was chosen to reveal the how ICTs are used in community empowerment in the case of Qaryah Thayyibah Learning Community, a school with a telecentre that in founded, run and funded by the community of Kalibening Village in Central Java, Indonesia. Through the application of the stakeholder theory, significant findings related to impact of ICTs for social change of the Qaryah Thayyibah Learning Community were revealed. These findings include patterns of access and participation of stakeholders in relation to ICTs. While ICTs had impacted the students of Qaryah Thayyibah, its impact had not yet been significantly experienced for the wider community outside of the school. Another important finding that emerged from the stakeholder analysis is the influence and value of the local champion in the establishment and sustainability of the ICTs initiative. In conclusion, the findings suggest that education such as that practiced in the Qaryah Thayyibah Learning Community can contribute in achieving rural empowerment, although the process may be long and complex. As this is an exploratory study investigating unquantifiable intangible impacts, further research can focus more on these intangible impacts through different lenses such as culture, power dynamics and social relations.
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Статья научная
This paper presents a new combined symmetric key cryptographic technique, which is generally an amalgamation of Bit Manipulation, generalized Modified Vernam Cipher, Single Bit Manipulation and Modified Caesar Cipher. The technique proposed here is basically an advanced and upgraded module of SD-AREE cryptographic method, which is based on Modified Caesar Cipher along with Bit Manipulation and the SD-AREE module is very useful in excluding any repetition pattern from a message that is to be encrypted. The proposed method, SD-AREE-I Cipher, is a complete cipher method and unlike its predecessor, SD-AREE, does not need to be added to other cryptographic methods to make those methods stronger. SD-AREE-I method is used to encrypt/decrypt different file formats and the results were very satisfactory. This method is unique and strong because the method contains feedback mechanism and generates new encrypted output every time even with slightest change in the input file (message). The proposed method can also be used for network security.
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STEM Project for Vehicle Image Segmentation Using Fuzzy Logic
Статья научная
A STEM project was implemented, which is intended for students of technical specialties to study the principles of building and using a computer system for segmentation of images of railway transport using fuzzy logic. The project consists of 4 stages, namely stage #1 "Reading images from video cameras using a personal computer or Raspberry Pi microcomputer", stage #2 "Digital image pre-processing (noise removal, contrast enhancement, contour selection)", stage #3 "Segmentation of images", stage #4 "Detection and analysis of objects on segmented images by means of fuzzy logic". Hardware and software tools have been developed for the implementation of the STEM project. A personal computer and a Raspberry Pi 3B+ microcomputer with attached video cameras were used as hardware. Software tools are implemented in the Python language using the Google Colab cloud platform. At each stage of the project, students deepen their knowledge and gain practical skills: they perform hardware and software settings, change program code, and process experimental images of vehicles. It is shown that the processing of experimental images ensures the correct selection of meaningful parts in images of vehicles, for example, windows and number plates in images of locomotives. Assessment of students' educational achievements was carried out by testing them before the start of the STEM project, as well as after the completion of the project. The topics of the test tasks corresponded to the topics of the stages of the STEM project. Improvements in educational achievements were obtained for all stages of the project.
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SXP: Simplified Extreme Programing Process Model
Статья научная
Extreme programming is one of the widely used agile models in the software industry. It can handle unclear and changing requirements with the good level of customer satisfaction. However Lack of documentation, poor architectural structure and less focus on design are its major drawbacks that affects its performance. Due to these problems it cannot be used for all kinds of projects. It is considered suitable for small and low risk projects. It also has some controversial practices that cannot be applied in each and every situation like pair programming and on-site customer. To overcome these limitations a modified version of XP called “Simplified Extreme Programming” is proposed in this paper. This model provides solution of these problems without affecting simplicity and agility of extreme programming.
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Scheduling of Generating Unit Commitment by Quantum-Inspired Evolutionary Algorithm
Статья научная
An Quantum-Inspired Evolutionary Algorithm (QEA) is presented for solving the unit commitment problem. The proposed method has been used to achieve the schedule of system units by considering optimal economic dispatch. The QEA method based on the quantum concepts such as Q-bit, present a better population diversity compared with previous evolutionary approaches, and uses quantum gates to achieve better solutions. The proposed method has been tested on a system with 10 generating units, and the results shows the effectiveness of algorithm compared with Other previous references. Furthermore, it can be used to solve the large-scale generating unit commitment problem.
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Scientific Approach of Prediction for Professions Using Machine Learning Classification Techniques
Статья научная
Astrology is a very ancient and traditional method of prediction that increases the interest of people continuously. The globe today, there are no common guidelines or principles for astrological prediction. Rather than setting universal principles and criteria for astrological prediction, astrologers focus on providing high-quality services to individuals but there is no guarantee of accuracy. Machine learning is providing the best result for analysis and prediction on many applications by the learning of computers. Prediction and classification make it possible for any learner to work on large, noisy, and complex datasets. The main motive of the paper is to introduce a scientific approach that reduces the drawback of the traditional approach and indicates the universal rules of prediction and proves the validity of astrology by the three classification techniques, Naïve Bayes, Logistic-R, and J48. It is a part of supervision learning that operates with cross-validation 10,12, and 14fold for calculating the terms 1) correctly classified instances (CCI), erroneously categorized instances (ECI), Mean absolute error (MAE), Root mean squared error (RMSE), and Relative absolute error (RAE). 2) True Positive Rate, False Positive Rate, Precision, and F-Measure values. 3) The MCC, ROC, and PRC area values. 4) To calculate the average weight of the three-class label professor, businessman, and doctor in terms of true positive rate, false-positive rate, precision, F-measure, PRC, and ROC area, 5) finally, we calculated the accuracy of each classification technique and compare which provide the better result. For this, we have collected the date of birth, place of birth, and time of birth of 100 persons who belong to different professions. 40 data of professors, 30 data of businessmen, and 30 data of doctors, prepare the horoscope of an individual with the help of software. For analysis, we create the datasheet in .csv format and apply this data sheet in the weka tool to check various parameters and the accuracy percentage of each classifier.
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Scrum with the spices of agile family: a systematic mapping
Статья научная
Agile mania has revolutionized the software industry. Scrum, being a widely adopted mainstream production process, has dominated other Agile family members. Both industrial and academic researchers eagerly tailored and adapted the Scrum framework in quest of software process improvement. Their burning desire for innovation drive them to integrate other software development models with it to leverage the forte of all the models combined and stifle the weaknesses. This paper aims at providing state-of-the-art insightful understanding of how practices from different Agile process models have been plugged into the Scrum framework to bring about improvements in different extents of development that ensued enhanced productivity, and product quality. To gain the in-depth perception, a systematic mapping study has been planned. This study will identify researches on hybrid models of Scrum within agile family, published between 2011 and 2017. Subsequently, these hybrid models of Scrum will be examined broadly by classifying and thematically analyzing the literature, and outcomes will be presented. This study will contribute a latest coarse-grained overview that in turn may guide researchers for future research endeavors.
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Secure and Dynamic Model for Book Searching on Cloud Computing as Mobile Augmented Reality
Статья научная
Availability of internet on different devices like smart phones like android based, IOs based, windows based and PDA etc. has brought into the evolution of mobile cloud computing, which is a vast side of research nowadays. Internet connectivity has become very easy with the evolution of Wi-Fi, everyone can access the internet using wireless connectivity. A major issue in wireless connectivity is the low level of encryption and low security. This might be a security risk for the sensitive data available on the cloud. There are mobile augmented reality systems based on cloud computing, we want to propose a dynamic framework for the security of cloud and live update data on cloud.
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Security Improvement of Object Oriented Design using Refactoring Rules
Статья научная
The main component of study is to confirm that how developed security model are helpful for security improvement of object oriented designs. Software refactoring is an essential activity during development and maintenance. It promotes the reengineering measures for improving quality and security of software. The researcher made an effort in this regard to develop security improvement guideline using refactoring activities for object oriented deign. The developed guidelines are helpful to control design complexity for improved security. A case study is adopted from refactoring example by fowler to implement the Security Improvement Guidelines (SIG). The developed Security Quantification Model (SQMOODC) is being used to calculate the quantified value of security at each step. The proposed model SQMOODC calculates the effective security index by ensuring that revised version of object oriented design is being influenced through security improvement guidelines. There is some possibility that original code segment may have some security flaws, anomalies and exploitable entities or vulnerable information that may influence security at design stage. SIG is helpful to cease the security flaws, anomalies, exploitable entities into refactored code segment. Each refactored steps of case study match the prediction of the impact for refactoring rules on security and the impact study for security through SQMOODC model legalize the effectiveness of developed model and security improvement guidelines. The validated results of statistical analysis with different case studies of object oriented designs reflect the usefulness and acceptability of developed models and guidelines.
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Selecting qualitative features of driver behavior via pareto analysis
Статья научная
Driver behavior is the main cause of road crashes; it is the key element that insures a better understanding and improves predictions of car accidents. The main goal of our study is to determine the set of driver behavior features that are the most encountered in literature; we were based on behavioral questionnaires as a source for these features. We selected the questionnaires that are most cited in literature and therefore proved their efficiency through many studies they were employed in. Then we extracted the features considered in their items and classified them by rate of appearance according to the Pareto & ABC principle. In the second part of our study we collaborated with the National Committee for Circulation Accident Prevention (CNPAC) of the Ministry of Transportation of Morocco in order to compare the findings we gathered from literature with the researches they administer. We prepared a questionnaire that contains the final set of features and we transmitted it to experts working in the road safety field to rate it according to their knowledge and experience. Data analysis showed significant differences in some features, which demonstrates the gap between theoretical results and field research.
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Semantic Annotation of Pedagogic Documents
Статья научная
To teach, teacher needs help for sharing these educational documents, and especially his knowledge. We present an approach to overcome the difficulty of sharing educational materials and facilitate access to content; we describe semantically these documents to make them accessible and available to different users. The main idea in our annotation approach is based on: (1) Identify key words in a document, to have a good presentation of the document, we extract the candidate words by applying a weighting process and another process using similarity measure, These keywords candidates are reconciled with ontology to determine the appropriate concepts. (2) As document reference generally other documents, we propagate the annotations of references for citing document. (3) A process of validation will be applied each time an annotation is added in order to keep the coherence of the base of annotation. After evaluation with several types of pedagogic documents, our approach achieved a good performance; this suggests that teachers can be greatly helped for the semantic annotation of their pedagogical documents.
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Semantic Management Information Modeling based on Theory of Concept Lattices
Статья научная
With the development of future Internet, it is of great significance to study how to realize unified management information modeling, in order to avoid a lot of repetitive work and standardize information modeling in network management domain. This paper discusses the problem from the ontology point of view and introduces the theory of concept lattices into the research on semantic management information modeling, which includes a) establishing an ontology-driven framework for semantic management information modeling, b) building unified management information modeling ontology based on concept lattices, and c) generating semantic models for network management information modeling using the theory of concept lattices.
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Semantic Question Generation Using Artificial Immunity
Статья научная
This research proposes an automatic question generation model for evaluating the understanding of semantic attributes in a sentence. The Semantic Role Labeling and Named Entity Recognition are used as a preprocessing step to convert the input sentence into a semantic pattern. The Artificial Immune System is used to build a classifier that will be able to classify the patterns according to the question type in the training phase. The question types considered here are the set of WH-questions like who, when, where, why, and how. A pattern matching phase is applied for selecting the best matching question pattern for the test sentence. The proposed model is tested against a set of sentences obtained from many sources such as the TREC 2007 dataset for question answering, Wikipedia articles, and English book of grade II preparatory. The experimental results of the proposed model are promising in determining the question type with classification accuracy reaching 95%, and 87% in generating the new question patterns.
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Semi-Supervised Personal Name Disambiguation Technique for the Web
Статья научная
Personal name ambiguity in the web arises when more than one person shares the same name. Personal name disambiguation involves disambiguating the name by clustering web page collection such that each cluster represents a person having the ambiguous name. In this paper, a personal name disambiguation technique that makes use of rich set of features like Nouns, Noun phrases, and frequent keywords as features is proposed. The proposed method consists of two phases namely clustering seed pages and then clustering the actual web page collection. In the first phase, seed pages representing different namesakes are clustered and in the second phase, web pages in the collection are clustered with the similar seed page clusters. The usage of seed pages increases the accuracy of clustering process. Since it is difficult to predict the number of clusters need to be formed beforehand, the proposed technique uses Elbow method to calculate the number of clusters. The efficiency of the proposed name disambiguation technique is tested using both synthetic and organic datasets. Experimental result shows the proposed method achieves robust results across different datasets and outperforms many existing methods.
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Sentiment Analysis of Twitter User Data on Punjab Legislative Assembly Election, 2017
Статья научная
Sentiment Analysis is the way of gathering and inspecting data based on the personal emotions, reviews, and contemplations. The sentimental analysis is also recognized as opinion mining since it mines the major feature from people opinions. There are various social networking platforms, out of which Twitter is praised by lawmakers, academicians, and journalists for its potential political values. In literature, numerous studies have been performed on the data ecstatic to elections on Twitter. The greater part of them has been on the U.S Presidential Elections where there are two main applicants who fight it out. Since individuals discuss so many political parties and candidates and their prospects too in rendered messages, the issues of distinguishing their political feeling become extensive and fascinating. Consideration of all these aspects along with a sheer volume of data propelled us to look into the data and find interesting inferences in it. To select the 117 members of the Punjab Legislative Assembly, Legislative Assembly election was held in Punjab, the State of India on 4 February 2017. As per the Election Commission, a total of 1.9 crore voters is eligible for voting in August 2016 in Punjab. The data set that is collected with the help of Twython was analyzed to find out trivial things and interesting patterns in the data. The central idea of this research paper is to carry out the sentiment analysis on Legislative Assembly election 2017 that was held in the Punjab, a state of India for the Political Parties like BJP, INC, and AAP. We have analyzed and fetch significant implications from the tweets gathered over the whole period of elections.
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Sentiment analysis on mobile phone reviews using supervised learning techniques
Статья научная
Opinion Mining or Sentiment Analysis is the process of mining emotions, attitudes, and opinions automatically from speech, text, and database sources through Natural Language Processing (NLP). Opinions can be given on anything. It may be a product, feature of a product or any sentiment view on a product. In this research, Mobile phone products reviews, fetched from Amazon.com, are mined to predict customer rating of the product based on its user reviews. This is performed by the sentiment classification of unlocked mobile reviews for the sake of opinion mining. Different opinion mining algorithms are used to identify the sentiments hidden in the reviews and comments for a specific unlocked mobile. Moreover, a performance analysis of Sentiment Classification algorithms is performed on the data set of mobile phone reviews. Results yields from this research provide the comparative analysis of eight different classifiers on the evaluation parameters of accuracy, recall, precision and F-measure. The Random Forest Classifiers offers more accurate predictions than others but LSTM and CNN also give better accuracy.
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